{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MMNx3H0s8k0IzNgZcfXW2ZSKypCiGN2pZ6qJBAYiLqjH++/aZ8b33mtz/I0da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\nCgAhhFSU/w948Nj/SOOrKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff24c245050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "data = load_iris()\n",
    "y = data.target\n",
    "X = data.data\n",
    "pca = PCA(n_components=2)\n",
    "reduced_X = pca.fit_transform(X)\n",
    "\n",
    "red_x, red_y = [], []\n",
    "blue_x, blue_y = [], []\n",
    "green_x, green_y = [], []\n",
    "for i in range(len(reduced_X)):\n",
    "    if y[i] == 0:\n",
    "        red_x.append(reduced_X[i][0])\n",
    "        red_y.append(reduced_X[i][1])\n",
    "    elif y[i] == 1:\n",
    "        blue_x.append(reduced_X[i][0])\n",
    "        blue_y.append(reduced_X[i][1])\n",
    "    else:\n",
    "        green_x.append(reduced_X[i][0])\n",
    "        green_y.append(reduced_X[i][1])\n",
    "plt.scatter(red_x, red_y, c='r', marker='x')\n",
    "plt.scatter(blue_x, blue_y, c='b', marker='D')\n",
    "plt.scatter(green_x, green_y, c='g', marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
